Evidence map›Paper›PMID 42337670›Full record

ArticleBMC psychology2026

Predicting heterosexual reactions to coming out in China.

Gu Li, Xiaoyu Huang, Cong Peng, Jiayu He

Abstract read
In one paragraph

Article in BMC psychology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Gu Li *Faculty of Arts and Sciences, NYU Shanghai, 567 West Yangsi Road, Pudong New Area, Shanghai, 200124, China. gu.li@nyu.edu.ORCID http://orcid.org/0000-0002-1994-1965
Xiaoyu Huang *Faculty of Arts and Sciences, NYU Shanghai, 567 West Yangsi Road, Pudong New Area, Shanghai, 200124, China.ORCID http://orcid.org/0009-0000-5063-4333
Cong PengFaculty of Arts and Sciences, NYU Shanghai, 567 West Yangsi Road, Pudong New Area, Shanghai, 200124, China.ORCID http://orcid.org/0000-0001-7485-973X
Jiayu HeFaculty of Arts and Sciences, NYU Shanghai, 567 West Yangsi Road, Pudong New Area, Shanghai, 200124, China.

Funding

New York University Shanghai Deans' Undergraduate Research FundNew York University Shanghai Start-up GrantShanghai Municipal Education Commission Shanghai Eastern Scholar Program Young Scholar Scheme
6 · The paper itself

Abstract

For many Chinese sexual minority men, coming out-disclosing their sexual orientation-is stressful because of concerns about how heterosexual recipients may react. This study examined predictors of those reactions and applied machine learning to model them. Between August 2022 and January 2023, we screened 54,902 individuals on a Chinese crowdsourcing platform and recruited 650 Chinese cisgender heterosexual participants who had experienced a coming-out encounter with a sexual minority man. Participants reported on 85 personal, contextual, and behavioral factors surrounding the encounter. Bivariate analyses and machine learning models revealed that background characteristics-such as the recipient's sex, age, and relationship to the discloser-were the strongest predictors of reactions. Smaller contributions emerged from select contextual parameters and coming-out strategies: negative reactions were linked to disclosures involving a love confession, assault, or substance use by disclosers; positive reactions were associated with recipients anticipating the disclosure and disclosers referencing a same-sex romantic relationship. The machine learning models captured complex interactions among predictors. These models achieved moderate predictive accuracy (ACC = 0.62-0.65, R

Indexed as

HeterosexualityInterpersonal RelationsSexual and Gender MinoritiesAdultChinaFemaleHumansMachine LearningMalePredictive Learning ModelsCARTComing outDisclosureLGBRandom forestsSexual minority

Identifiers

PMID42337670
PMCPMC13560178

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.